6 citations · 11 across the 8 of their papers we have counts for
8 papers
Gradient-Based Program Synthesis with Neurally Interpreted Languages
Matthew V. Macfarlane, Clément Bonnet, Herke van Hoof +1
A central challenge in program induction has long been the trade-off between symbolic and neural approaches. Symbolic methods offer compositional generalisation and data efficiency…
From Text to Pose to Image: Improving Diffusion Model Control and Quality
Clément Bonnet, Ariel N. Lee, Franck Wertel +3
In the last two years, text-to-image diffusion models have become extremely popular. As their quality and usage increase, a major concern has been the need for better output contro…
Searching Latent Program Spaces
Matthew V Macfarlane, Clement Bonnet
General intelligence requires systems that acquire new skills efficiently and generalize beyond their training distributions. Although program synthesis approaches have strong gene…
Combinatorial Optimization with Policy Adaptation using Latent Space Search
Felix Chalumeau, Shikha Surana, Clement Bonnet +4
Combinatorial Optimization underpins many real-world applications and yet, designing performant algorithms to solve these complex, typically NP-hard, problems remains a significant…
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
Clément Bonnet, Daniel Luo, Donal Byrne +21
Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms. In modern RL research, there is a need for…
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value Function
Clément Bonnet, Laurence Midgley, Alexandre Laterre
Meta-gradient Reinforcement Learning (RL) allows agents to self-tune their hyper-parameters in an online fashion during training. In this paper, we identify a bias in the meta-grad…
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization
Nathan Grinsztajn, Daniel Furelos-Blanco, Shikha Surana +2
Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as it removes the need for expert knowledge or pre-solved instances. However, it is unreal…
One Step at a Time: Pros and Cons of Multi-Step Meta-Gradient Reinforcement Learning
Clément Bonnet, Paul Caron, Thomas Barrett +2
Self-tuning algorithms that adapt the learning process online encourage more effective and robust learning. Among all the methods available, meta-gradients have emerged as a promis…